Advanced semiconductor industry is continuing to integrate thinner and multi-stack films of novel material compositions (e.g. SiGe, HKMG, 3D FinFet, etc.). This is one way to continue the performance scaling to ensure that advanced nodes like 1X and beyond keep following the Moore's law.
Process control of ultra-thin films in the 1-2 nm range require a Total Measurement Uncertainty (TMU1—relative accuracy metric) and Fleet Matching on the order of 0.1 A. Optical metrology techniques (such as Spectral Ellipsometry—currently the throughput-intensive workhorse of thin film measurements) are reaching their performance limits. As an example, ability to accurately identify 0.1 Angstrom change in film thickness is fundamentally limited by cross-talk with other parameters of the film stack (thickness of other layers, variable materials composition) as well as limited optical sensitivity.
There is a need in the art in a novel approach for monitoring/measurement thin films' parameters, especially for the films having changeable parameter(s) such as for example material concentration. Moreover, the monitoring technique should preferably enable effective process control of the manufacture of thin film based structures progressing on a production line. In other words, the measurement technique should be effectively operable in real-time (or in-line or on-tool) measurement mode. In this connection, it should be noted that the term “in-line” or “on-tool” used herein refers to a measurement mode which is an alternative to a measurement by a stand-alone measurement tool operating in an off-line mode.
Optical metrology techniques are based on measurement of properties of light reflected from a sample. For thin film measurements, they rely on the contrast between optical properties of different films to model, de-convolute and individually extract the film thickness, and are typically extended to profile measurements of periodic structures using the properties of scattered light (scatterometry, also known as optical critical dimension—OCD). While such methods are fast and well suited for advanced process control (APC), they rely on assumptions about optical properties and geometry. In particular, the performance for ultra-thin films with variable composition is typically subjected to errors due to inability to separate the effects of optical properties changes from thickness change. This is schematically illustrated in FIG. 1 showing the diagram of an optical measurement and data interpretation of a planar or periodic structure.
The figure shows, in a self-explanatory manner, the on-tool (or on-line) and off-line measurement schemes. In the on-line measurement scheme, optical spectral measurements (e.g. polarized based measurements) are applied to a sample (e.g. semiconductor wafer), and a fitting procedure is applied to measured spectral data and upon identifying the best-fit spectral data from the library, determining the corresponding structure parameters (e.g. CD, height, SWA, etc.). As also shown in the figure, the spectral library is built using off-line measurement scheme. The off-line measurement scheme utilizes the model creation (modeled structures on target) using such input data as film stack information, film optical properties, and geometry information. The model may then be optimized using various known approaches, for creating/updating the spectral library, i.e. theoretical spectral data.
X-ray methods come in different flavors. Particularly suitable for thin films, X-Ray Photoelectron Spectroscopy (XPS) measures the spectral response of photoelectrons generated by X-Ray irradiation of the target. Such photoelectrons are rapidly recombining inside the material, the net effect being that only electrons generated in the superficial ˜10 nm of material are able to escape the stack and be detected. Signals from different atomic species/molecules are recorded as a function of energy and ascribed to different layers of the stack, consistent with the physical interaction between X-Rays, electrons and the different materials. This is illustrated schematically in FIG. 2 showing, in a self-explanatory manner, a diagram of an XPS measurement and measured data. XPS measurements can thus independently extract both composition (relative number of atoms of different species) and layer thicknesses of multi-layer ultra-thin films. However, XPS technique does not provide desirable throughput required for high volume manufacturing (HVM). In other words, the XPS measurements are practically unsuitable for on-line measurements mode on structures progressing on a production line for the production process control.
Reference is made to FIGS. 3A and 3B demonstrating the effect of measurement uncertainty on the HVM. FIG. 3A shows measurement subjected to precision (type A) errors, which can be typically minimized by reducing the number of floating parameters. FIG. 3B shows measurement subjected to accuracy (type B) errors. Any measurement is subject to different sources of errors. According to the specific circumstances of model-based metrology, type A errors are easily quantifiable based on statistical analysis of random noise—precision. This type of error is typically minimized by reducing the number of possible varying model parameters (and correspondingly increasing the stability of values for the key parameters when performing repeat measurements). Type B errors (accuracy indicator) typically arise from crosstalk between key parameters (floating) and some of the fixed parameters (whole corresponding physical sample parameters actually change). Such fixed parameters can include optical properties of materials. In HVM environment, such errors are largely hidden, and can lead to incorrect wafer disposition. As shown in FIG. 3B, comparison to reference can help establish the correlation calibration to “truth” (arrow G) during recipe setup. However, inadvertent (unknown) changes in some of the fixed model parameters during HVM can induce corresponding changes in the measured values; this invalidates the original relationship to the “truth” and thus can trigger incorrect wafer disposition.
Let us define a “reference-level” measurement as being invariant to inadvertent changes in other parameters as resulting from normal process changes in HVM and at the same time being super sensitive to measurement parameters in question. Examples of such measurement can be considered X-Ray Fluorescence (XRF) which provides the total number of atoms of a certain species regardless of atoms from other species present in the measured sample, or XPS which probes the atomic species present in the superficial layers of the sample regardless of the layer stack beyond ˜10 nm in depth. Ideally, in-line metrology would provide a “reference-level” measurement. In practice, there is always a trade-off between measurement speed and performance, which in many cases is solved in favor of the higher throughput, higher risk metrology.
Hybrid Metrology is the practice of combining two or more metrology tools that measure the same or similar structures. Data is shared between toolsets in complementary way to enhance metrology performance, and enables measurement of complex structures that cannot be measured with enough performance by any of the individual toolset. Most of hybrid metrology work to date combined non-reference metrologies (CD-SEM and OCD).